What is the result of word tokenization after removing the stop words from the sentence: "We had to stay at home for two weeks."?

What is the result of word tokenization after removing the stop words from the sentence: "We had to stay at home for two weeks."?

• A. ["stay", "at", "home", "two", "weeks"]
• B. ["stay", "home", "two", "weeks"]
• C. ["two", "weeks"]
• D. ["we", "had", "to", "stay", "at", "home", "for", "two", "weeks"]

Answer: (B)

In the context of natural language processing, word tokenization involves breaking down a sentence into individual words or tokens. After this initial step, the next task is often removing stop words, which are common words that typically do not contribute significant meaning to the sentence, such as "we," "had," "to," "at," "for," etc. In the sentence "We had to stay at home for two weeks," the word tokenization results in the following tokens: ["We", "had", "to", "stay", "at", "home", "for", "two", "weeks"]. When stop words are removed, we focus on the more meaningful words. The remaining words are typically the nouns, verbs, adjectives, and adverbs. The process identifies "stay," "home," "two," and "weeks" as the significant tokens. These words carry the main content of the message, while the stop words are excluded. Thus, the final result after removing the stop words is ["stay", "home", "two", "weeks"], which captures the essence of the original sentence without the common filler words. This process of filtering out less meaningful words enhances the quality of data for analyses in various applications, such as text mining

In the context of natural language processing, word tokenization involves breaking down a sentence into individual words or tokens. After this initial step, the next task is often removing stop words, which are common words that typically do not contribute significant meaning to the sentence, such as "we," "had," "to," "at," "for," etc.

In the sentence "We had to stay at home for two weeks," the word tokenization results in the following tokens: ["We", "had", "to", "stay", "at", "home", "for", "two", "weeks"]. When stop words are removed, we focus on the more meaningful words. The remaining words are typically the nouns, verbs, adjectives, and adverbs.

The process identifies "stay," "home," "two," and "weeks" as the significant tokens. These words carry the main content of the message, while the stop words are excluded. Thus, the final result after removing the stop words is ["stay", "home", "two", "weeks"], which captures the essence of the original sentence without the common filler words.

This process of filtering out less meaningful words enhances the quality of data for analyses in various applications, such as text mining